{"id":"W4399322880","doi":"10.25144/22193","title":"EVALUATING NOISE FROM CLAY PIGEON SHOOTS: PRACTICAL EXPERIENCE AND LITERATURE SURVEYS","year":2024,"lang":"en","type":"article","venue":"","topic":"Tree Root and Stability Studies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Martec (Canada)","funders":"","keywords":"Noise (video); Computer science; Artificial intelligence; Image (mathematics)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003354884,0.0001275882,0.0001287682,0.00003131739,0.00005199104,0.000205211,0.00003936521,0.00006577512,0.0001150192],"category_scores_gemma":[0.0001994446,0.00009877587,0.00002949993,0.0001544109,0.0000338061,0.0002782255,0.0000442604,0.0001968491,0.00002276214],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002614888,"about_ca_system_score_gemma":0.000009739354,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004412466,"about_ca_topic_score_gemma":0.0005729881,"domain_scores_codex":[0.9992452,0.00007025302,0.0001425546,0.0002318645,0.0001460733,0.0001640544],"domain_scores_gemma":[0.9993485,0.0004250444,0.000004480127,0.0001390519,0.00002813071,0.00005478833],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0000633587,0.0001687388,0.2968384,0.001517993,0.0008212602,0.0006200778,0.1773101,0.002034695,0.08300839,0.003851034,0.01756128,0.4162047],"study_design_scores_gemma":[0.0005045405,0.0001547177,0.5634061,0.0005437933,0.00009034029,0.00003502011,0.003398555,0.4138379,0.005621364,0.002421605,0.009024345,0.0009616843],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9815576,0.00791047,0.006521369,0.0003503321,0.0004716041,0.00009248107,0.00002321573,0.0005014831,0.002571442],"genre_scores_gemma":[0.9969608,0.00007962887,0.002620849,0.00001050296,0.00011975,0.00002016582,0.0000092667,0.00001524031,0.0001637688],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.415243,"threshold_uncertainty_score":0.4027963,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05474785279907191,"score_gpt":0.3571590093272705,"score_spread":0.3024111565281986,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}